{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一、train_queries数据集\n",
    "### sid：会话id\n",
    "### pid：配置文件id\n",
    "### req_time：查询时间戳\n",
    "### o：起始点坐标\n",
    "### d：终点坐标"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1    数据大小&类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_queries = pd.read_csv('data_set_phase1//train_queries.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sid</th>\n",
       "      <th>pid</th>\n",
       "      <th>req_time</th>\n",
       "      <th>o</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3000821</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-11-02 17:54:30</td>\n",
       "      <td>116.29,39.97</td>\n",
       "      <td>116.32,39.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3085857</td>\n",
       "      <td>210736.0</td>\n",
       "      <td>2018-11-16 10:53:10</td>\n",
       "      <td>116.39,39.84</td>\n",
       "      <td>116.33,39.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2944522</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-10-06 10:33:58</td>\n",
       "      <td>116.31,39.93</td>\n",
       "      <td>116.27,40.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>559931</td>\n",
       "      <td>202427.0</td>\n",
       "      <td>2018-11-23 14:54:11</td>\n",
       "      <td>116.27,39.88</td>\n",
       "      <td>116.39,39.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2819352</td>\n",
       "      <td>172251.0</td>\n",
       "      <td>2018-10-30 11:48:41</td>\n",
       "      <td>116.34,39.96</td>\n",
       "      <td>116.37,39.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sid       pid             req_time             o             d\n",
       "0  3000821       NaN  2018-11-02 17:54:30  116.29,39.97  116.32,39.96\n",
       "1  3085857  210736.0  2018-11-16 10:53:10  116.39,39.84  116.33,39.79\n",
       "2  2944522       NaN  2018-10-06 10:33:58  116.31,39.93  116.27,40.00\n",
       "3   559931  202427.0  2018-11-23 14:54:11  116.27,39.88  116.39,39.90\n",
       "4  2819352  172251.0  2018-10-30 11:48:41  116.34,39.96  116.37,39.86"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_queries.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500000, 5)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_queries.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sid           int64\n",
       "pid         float64\n",
       "req_time     object\n",
       "o            object\n",
       "d            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_queries.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 结论：\n",
    "#### 1、数据中有一个int64类型的数据，一个float64类型的数据，三个object类型数据\n",
    "#### 2、一共有500000条数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2     数据缺失值情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sid              0\n",
       "pid         163979\n",
       "req_time         0\n",
       "o                0\n",
       "d                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_queries.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.327958"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "163979/500000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 结论：pid存在163979个缺失值，缺失比例为32.7958%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3  数据nunique的分布"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### nuinque()是查看该序列(axis=0/1对应着列或行)的不同值的数量。用这个函数可以查看数据有多少个不同值。数据nunique的分布的比例，可以判断某一列的特征是否唯一"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sid         500000\n",
       "pid          46191\n",
       "req_time    464211\n",
       "o             5460\n",
       "d             4923\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_queries.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sid         1.000000\n",
       "pid         0.092382\n",
       "req_time    0.928422\n",
       "o           0.010920\n",
       "d           0.009846\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_queries.nunique()/train_queries.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 结论：\n",
    "#### 1、sid是唯一的\n",
    "#### 2、pid不同值占了9.2382%\n",
    "#### 3、o和d的不同值分别占了1.092%和0.9846%，说明起始点坐标和终点坐标有很多相同值；大概是相同地点搜索路线的用户有很多，相同起点和相同目的地的用户也很多"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.4 o和d探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "116.37,39.86    5993\n",
       "116.32,39.89    5930\n",
       "116.42,39.90    5819\n",
       "116.41,39.91    5616\n",
       "116.37,39.91    5246\n",
       "Name: o, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_queries['o'].value_counts()[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "116.32,39.89    16503\n",
       "116.37,39.86    13124\n",
       "116.42,39.90    11256\n",
       "116.39,39.90     7575\n",
       "116.39,39.91     7571\n",
       "Name: d, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_queries['d'].value_counts()[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.4.1  o和d的分布探索"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 为了对经纬度有个大致了解，将经纬度的信息进行KMeans聚类，并用散点图进行绘制"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from pandas import Series, DataFrame\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "\n",
    "o_x_list = []\n",
    "o_y_list = []\n",
    "\n",
    "d_x_list = []\n",
    "d_y_list = []\n",
    "\n",
    "# 将经纬度坐标提取出来\n",
    "for i in range(train_queries.shape[0]):\n",
    "    o_x_list.append(float(train_queries['o'][i].split(',')[0]))\n",
    "    o_y_list.append(float(train_queries['o'][i].split(',')[1]))\n",
    "    \n",
    "    d_x_list.append(float(train_queries['d'][i].split(',')[0]))\n",
    "    d_y_list.append(float(train_queries['d'][i].split(',')[1]))\n",
    "    \n",
    "o_data = pd.DataFrame.from_dict({'x': o_x_list, 'y': o_y_list})\n",
    "d_data = pd.DataFrame.from_dict({'x': d_x_list, 'y': d_y_list})\n",
    "\n",
    "# KMeans聚类\n",
    "model_o = KMeans(n_clusters=7, init='k-means++', n_init=5)\n",
    "model_d = KMeans(n_clusters=7, init='k-means++', n_init=5)\n",
    "y_pre_o = model_o.fit_predict(o_data)\n",
    "y_pre_d = model_d.fit_predict(d_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26f99fe6dd8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画散点图图\n",
    "def plotScatter(data, y_pre, x_list, y_list):\n",
    "    matplotlib.rcParams['font.sans-serif'] = ['SimHei']\n",
    "    matplotlib.rcParams['axes.unicode_minus'] = False\n",
    "    cm = matplotlib.colors.ListedColormap(['c','b','r','g','m','y'])\n",
    "    plt.scatter(data['x'], data['y'], c=y_pre, cmap=cm, edgecolors='none')\n",
    "    x_min = np.min(x_list)\n",
    "    x_max = np.max(x_list)\n",
    "    y_min = np.min(y_list)\n",
    "    y_max = np.max(y_list)\n",
    "    plt.xticks(np.linspace(x_min,x_max,8,endpoint=True))\n",
    "    plt.yticks(np.linspace(y_min,y_max,9,endpoint=True))\n",
    "    plt.xlabel('纬度', fontsize=16)\n",
    "    plt.ylabel('经度', fontsize=16)\n",
    "plt.figure(figsize=(15, 10))\n",
    "ax1 = plt.subplot(2,2,1)\n",
    "plotScatter(o_data, y_pre_o, o_x_list, o_y_list)\n",
    "ax1.set_title('起点（o）经纬度分布', fontsize=18)\n",
    "ax2 = plt.subplot(2,2,2)\n",
    "plotScatter(d_data, y_pre_d, d_x_list, d_y_list)\n",
    "ax2.set_title('终点（d）经纬度分布', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 从上图可以看出，中心区较为密集，多为商场等大型场所，所以点很多；较远区域有很多散点，多为上班族下班回郊区所造成。而且起点（o）的分布与终点（d）的分布类似。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 二、train_plans数据集\n",
    "### sid：会话id\n",
    "### plan_time：计划推荐时间戳\n",
    "### plans：推荐模式，包括distance(距离m)、price(价格c)、eta(预计所需时间s)、transport_mode(出行模式)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1  数据类型和大小"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_plans = pd.read_csv('data_set_phase1//train_plans.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sid</th>\n",
       "      <th>plan_time</th>\n",
       "      <th>plans</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1709112</td>\n",
       "      <td>2018-11-04 11:45:04</td>\n",
       "      <td>[{\"distance\": 32303, \"price\": 600, \"eta\": 5087...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3327773</td>\n",
       "      <td>2018-10-16 19:09:29</td>\n",
       "      <td>[{\"distance\": 3427, \"price\": 300, \"eta\": 627, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3285959</td>\n",
       "      <td>2018-11-25 15:05:22</td>\n",
       "      <td>[{\"distance\": 36234, \"price\": 1100, \"eta\": 931...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1616170</td>\n",
       "      <td>2018-10-03 14:42:40</td>\n",
       "      <td>[{\"distance\": 11450, \"price\": 400, \"eta\": 2801...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>351369</td>\n",
       "      <td>2018-10-05 19:52:37</td>\n",
       "      <td>[{\"distance\": 18495, \"price\": 600, \"eta\": 4271...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sid            plan_time  \\\n",
       "0  1709112  2018-11-04 11:45:04   \n",
       "1  3327773  2018-10-16 19:09:29   \n",
       "2  3285959  2018-11-25 15:05:22   \n",
       "3  1616170  2018-10-03 14:42:40   \n",
       "4   351369  2018-10-05 19:52:37   \n",
       "\n",
       "                                               plans  \n",
       "0  [{\"distance\": 32303, \"price\": 600, \"eta\": 5087...  \n",
       "1  [{\"distance\": 3427, \"price\": 300, \"eta\": 627, ...  \n",
       "2  [{\"distance\": 36234, \"price\": 1100, \"eta\": 931...  \n",
       "3  [{\"distance\": 11450, \"price\": 400, \"eta\": 2801...  \n",
       "4  [{\"distance\": 18495, \"price\": 600, \"eta\": 4271...  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_plans.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(491054, 3)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_plans.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sid           int64\n",
       "plan_time    object\n",
       "plans        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_plans.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sid          0\n",
       "plan_time    0\n",
       "plans        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_plans.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sid          491054\n",
       "plan_time    455427\n",
       "plans        489563\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_plans.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 结论：\n",
    "#### 1、数据集总共491054条数据\n",
    "#### 2、数据集中包含一个int64数据类型，两个object数据类型\n",
    "#### 3、无缺失值\n",
    "#### 4、sid唯一，plan_time和plans有不同程度的相同数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2  plans分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### plans是以list形式存储，读取出来是string类型，所以用eval对plans的类型进行还原，并抽取里面的特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "plans = pd.DataFrame()\n",
    "for i in range(1000):# train_plans.shape[0]\n",
    "    sid = train_plans['sid'][i]\n",
    "    for j in range(len(eval(train_plans['plans'][i]))):\n",
    "        distance = (eval(train_plans['plans'][i])[j])['distance']\n",
    "        price = (eval(train_plans['plans'][i])[j])['price']\n",
    "        eta = (eval(train_plans['plans'][i])[j])['eta']\n",
    "        transport_mode = (eval(train_plans['plans'][i])[j])['transport_mode']\n",
    "        plan = {'sid': sid, 'transport_mode': transport_mode, 'distance': distance, 'price': price, 'eta': eta, 'rank': j+1}\n",
    "        plan_dt = pd.DataFrame.from_dict(plan, orient='index').T\n",
    "        plans = pd.concat([plans, plan_dt], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sid</th>\n",
       "      <th>transport_mode</th>\n",
       "      <th>distance</th>\n",
       "      <th>price</th>\n",
       "      <th>eta</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1709112</td>\n",
       "      <td>9</td>\n",
       "      <td>32303</td>\n",
       "      <td>600</td>\n",
       "      <td>5087</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1709112</td>\n",
       "      <td>3</td>\n",
       "      <td>33678</td>\n",
       "      <td>0</td>\n",
       "      <td>3149</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1709112</td>\n",
       "      <td>4</td>\n",
       "      <td>33678</td>\n",
       "      <td>10000</td>\n",
       "      <td>3269</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1709112</td>\n",
       "      <td>2</td>\n",
       "      <td>32099</td>\n",
       "      <td>600</td>\n",
       "      <td>5996</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1709112</td>\n",
       "      <td>1</td>\n",
       "      <td>30446</td>\n",
       "      <td>900</td>\n",
       "      <td>6494</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sid transport_mode distance  price   eta rank\n",
       "0  1709112              9    32303    600  5087    1\n",
       "1  1709112              3    33678      0  3149    2\n",
       "2  1709112              4    33678  10000  3269    3\n",
       "3  1709112              2    32099    600  5996    4\n",
       "4  1709112              1    30446    900  6494    5"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plans.price[plans['price']=='']=0\n",
    "plans.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3  transport_mode猜测"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 画出每一种transport_mode的平均速度直方图和平均价格直方图，猜测每一种mode是什么"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26f9a06c7f0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 计算每种mode的平均价格\n",
    "p = []\n",
    "for i in range(11):\n",
    "    pri = plans.price[plans['transport_mode']==i+1].sum()/len(plans.price[plans['transport_mode']==i+1])\n",
    "    p.append(pri)\n",
    "    \n",
    "# 计算每种mode的平均速度\n",
    "s = []\n",
    "for i in range(11):\n",
    "    speed = plans.distance[plans['transport_mode']==i+1].sum()/plans.eta[plans['transport_mode']==i+1].sum()\n",
    "    s.append(speed)\n",
    "\n",
    "# 画直方图\n",
    "plt.figure(figsize=(12,9))\n",
    "x_label = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11']\n",
    "ax1 = plt.subplot(2,2,1)\n",
    "ax1.bar(range(len(p)), p, tick_label=x_label)\n",
    "ax1.set_title('每种mode平均价格直方图', fontsize=18)\n",
    "ax1.set_xlabel('mode', fontsize=16)\n",
    "ax1.set_ylabel('price', fontsize=16)\n",
    "\n",
    "ax2 = plt.subplot(2,2,2)\n",
    "ax2.bar(range(len(s)), s, tick_label=x_label)\n",
    "ax2.set_title('每种mode平均速度直方图', fontsize=18)\n",
    "ax2.set_xlabel('mode', fontsize=16)\n",
    "ax2.set_ylabel('speed', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 通过观察上图，mode5可能是步行，因为价格为0，速度最慢；mode6可能是自行车，因为价格为0，速度还算可以；mode3可能是自驾，因为价格为0，速度比较快，自己家的车不需要向外支出费用。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.4  train_queries和train_plans中的sid和时间戳探索"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### train_queries和train_plans都存在一个sid和一个时间戳，我们想探索一下sid和时间戳是否有很大差别"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "q_p = pd.merge(train_queries, train_plans, on='sid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sid</th>\n",
       "      <th>pid</th>\n",
       "      <th>req_time</th>\n",
       "      <th>o</th>\n",
       "      <th>d</th>\n",
       "      <th>plan_time</th>\n",
       "      <th>plans</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3000821</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-11-02 17:54:30</td>\n",
       "      <td>116.29,39.97</td>\n",
       "      <td>116.32,39.96</td>\n",
       "      <td>2018-11-02 17:54:30</td>\n",
       "      <td>[{\"distance\": 5219, \"price\": 300, \"eta\": 1367,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3085857</td>\n",
       "      <td>210736.0</td>\n",
       "      <td>2018-11-16 10:53:10</td>\n",
       "      <td>116.39,39.84</td>\n",
       "      <td>116.33,39.79</td>\n",
       "      <td>2018-11-16 10:53:10</td>\n",
       "      <td>[{\"distance\": 13864, \"price\": 600, \"eta\": 3221...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2944522</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-10-06 10:33:58</td>\n",
       "      <td>116.31,39.93</td>\n",
       "      <td>116.27,40.00</td>\n",
       "      <td>2018-10-06 10:33:58</td>\n",
       "      <td>[{\"distance\": 12294, \"price\": 400, \"eta\": 2472...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>559931</td>\n",
       "      <td>202427.0</td>\n",
       "      <td>2018-11-23 14:54:11</td>\n",
       "      <td>116.27,39.88</td>\n",
       "      <td>116.39,39.90</td>\n",
       "      <td>2018-11-23 14:54:11</td>\n",
       "      <td>[{\"distance\": 14853, \"price\": 1700, \"eta\": 291...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2819352</td>\n",
       "      <td>172251.0</td>\n",
       "      <td>2018-10-30 11:48:41</td>\n",
       "      <td>116.34,39.96</td>\n",
       "      <td>116.37,39.86</td>\n",
       "      <td>2018-10-30 11:48:41</td>\n",
       "      <td>[{\"distance\": 12882, \"price\": 600, \"eta\": 3211...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sid       pid             req_time             o             d  \\\n",
       "0  3000821       NaN  2018-11-02 17:54:30  116.29,39.97  116.32,39.96   \n",
       "1  3085857  210736.0  2018-11-16 10:53:10  116.39,39.84  116.33,39.79   \n",
       "2  2944522       NaN  2018-10-06 10:33:58  116.31,39.93  116.27,40.00   \n",
       "3   559931  202427.0  2018-11-23 14:54:11  116.27,39.88  116.39,39.90   \n",
       "4  2819352  172251.0  2018-10-30 11:48:41  116.34,39.96  116.37,39.86   \n",
       "\n",
       "             plan_time                                              plans  \n",
       "0  2018-11-02 17:54:30  [{\"distance\": 5219, \"price\": 300, \"eta\": 1367,...  \n",
       "1  2018-11-16 10:53:10  [{\"distance\": 13864, \"price\": 600, \"eta\": 3221...  \n",
       "2  2018-10-06 10:33:58  [{\"distance\": 12294, \"price\": 400, \"eta\": 2472...  \n",
       "3  2018-11-23 14:54:11  [{\"distance\": 14853, \"price\": 1700, \"eta\": 291...  \n",
       "4  2018-10-30 11:48:41  [{\"distance\": 12882, \"price\": 600, \"eta\": 3211...  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q_p.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(491054, 7)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q_p.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 比较两个时间戳\n",
    "import datetime\n",
    "time = []\n",
    "for i in range(q_p.shape[0]):\n",
    "    req_time = datetime.datetime.strptime(q_p['req_time'][i], \"%Y-%m-%d %H:%M:%S\")\n",
    "    plan_time = datetime.datetime.strptime(q_p['plan_time'][i], \"%Y-%m-%d %H:%M:%S\")\n",
    "    time.append(req_time-plan_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 结论：\n",
    "#### 1、train_plans中的sid都在train_queries中\n",
    "#### 2、req_time和plan_time基本上是一样的（99%左右），有个别不一样的情况，可能是因为网速或查询地点有问题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 三、train_clicks数据集\n",
    "### sid：会话（id）\n",
    "### click_time：点击时间\n",
    "### click_mode：点击模式"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1  数据集大小和类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_clicks = pd.read_csv('data_set_phase1//train_clicks.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sid</th>\n",
       "      <th>click_time</th>\n",
       "      <th>click_mode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2848914</td>\n",
       "      <td>2018-11-17 18:42:17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2629085</td>\n",
       "      <td>2018-10-12 16:28:13</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>602598</td>\n",
       "      <td>2018-11-11 16:38:42</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022975</td>\n",
       "      <td>2018-10-14 12:28:11</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>988425</td>\n",
       "      <td>2018-11-12 16:47:30</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sid           click_time  click_mode\n",
       "0  2848914  2018-11-17 18:42:17           1\n",
       "1  2629085  2018-10-12 16:28:13           3\n",
       "2   602598  2018-11-11 16:38:42           2\n",
       "3  2022975  2018-10-14 12:28:11           9\n",
       "4   988425  2018-11-12 16:47:30           7"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_clicks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(453336, 3)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_clicks.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sid            int64\n",
       "click_time    object\n",
       "click_mode     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_clicks.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sid           0\n",
       "click_time    0\n",
       "click_mode    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_clicks.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sid           453336\n",
       "click_time    422100\n",
       "click_mode        11\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_clicks.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 结论：\n",
    "#### 1、数据集一共有453336条数据\n",
    "#### 2、数据集有两个int类型数据和一个object数据\n",
    "#### 3、无缺失值\n",
    "#### 4、sid是唯一的，click_time有6.89%是不唯一的，click_mode总共有11种"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 标签分布及特点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26f9fa75470>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = train_clicks['click_mode'].value_counts().values\n",
    "plt.figure(figsize=(14,12))\n",
    "\n",
    "x_label = ['2', '7', '1', '9', '5', '3', '10', '4', '6', '11', '8']\n",
    "ax1 = plt.subplot(2,2,1)\n",
    "ax1.bar(range(len(y)), y, tick_label=x_label)\n",
    "ax1.set_title('标签分布直方图', fontsize=18)\n",
    "ax1.set_xlabel('mode', fontsize=16)\n",
    "ax1.set_ylabel('counts', fontsize=16)\n",
    "\n",
    "ax2 = plt.subplot(2,2,2)\n",
    "ax2.pie(y, labels=x_label,autopct='%3.1f%%', shadow=False)\n",
    "ax2.set_title('标签分布饼状图', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2、7、1、9、5是最多的；2比7的价格低，而且速度也较快；1比7的价格低，但是速度慢；9的价格相对2、7、1较低，速度相对2、7、1也较快，但选择的人数却相对较少。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3  细节探索"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### train_queries、train_plans、train_clicks中都有时间戳，查看时间戳是否有差别"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "q_p_c = pd.merge(q_p, train_clicks, on='sid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sid</th>\n",
       "      <th>pid</th>\n",
       "      <th>req_time</th>\n",
       "      <th>o</th>\n",
       "      <th>d</th>\n",
       "      <th>plan_time</th>\n",
       "      <th>plans</th>\n",
       "      <th>click_time</th>\n",
       "      <th>click_mode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3000821</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-11-02 17:54:30</td>\n",
       "      <td>116.29,39.97</td>\n",
       "      <td>116.32,39.96</td>\n",
       "      <td>2018-11-02 17:54:30</td>\n",
       "      <td>[{\"distance\": 5219, \"price\": 300, \"eta\": 1367,...</td>\n",
       "      <td>2018-11-02 17:54:32</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3085857</td>\n",
       "      <td>210736.0</td>\n",
       "      <td>2018-11-16 10:53:10</td>\n",
       "      <td>116.39,39.84</td>\n",
       "      <td>116.33,39.79</td>\n",
       "      <td>2018-11-16 10:53:10</td>\n",
       "      <td>[{\"distance\": 13864, \"price\": 600, \"eta\": 3221...</td>\n",
       "      <td>2018-11-16 10:53:32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2944522</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-10-06 10:33:58</td>\n",
       "      <td>116.31,39.93</td>\n",
       "      <td>116.27,40.00</td>\n",
       "      <td>2018-10-06 10:33:58</td>\n",
       "      <td>[{\"distance\": 12294, \"price\": 400, \"eta\": 2472...</td>\n",
       "      <td>2018-10-06 10:34:23</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>559931</td>\n",
       "      <td>202427.0</td>\n",
       "      <td>2018-11-23 14:54:11</td>\n",
       "      <td>116.27,39.88</td>\n",
       "      <td>116.39,39.90</td>\n",
       "      <td>2018-11-23 14:54:11</td>\n",
       "      <td>[{\"distance\": 14853, \"price\": 1700, \"eta\": 291...</td>\n",
       "      <td>2018-11-23 14:54:18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2819352</td>\n",
       "      <td>172251.0</td>\n",
       "      <td>2018-10-30 11:48:41</td>\n",
       "      <td>116.34,39.96</td>\n",
       "      <td>116.37,39.86</td>\n",
       "      <td>2018-10-30 11:48:41</td>\n",
       "      <td>[{\"distance\": 12882, \"price\": 600, \"eta\": 3211...</td>\n",
       "      <td>2018-10-30 11:49:04</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sid       pid             req_time             o             d  \\\n",
       "0  3000821       NaN  2018-11-02 17:54:30  116.29,39.97  116.32,39.96   \n",
       "1  3085857  210736.0  2018-11-16 10:53:10  116.39,39.84  116.33,39.79   \n",
       "2  2944522       NaN  2018-10-06 10:33:58  116.31,39.93  116.27,40.00   \n",
       "3   559931  202427.0  2018-11-23 14:54:11  116.27,39.88  116.39,39.90   \n",
       "4  2819352  172251.0  2018-10-30 11:48:41  116.34,39.96  116.37,39.86   \n",
       "\n",
       "             plan_time                                              plans  \\\n",
       "0  2018-11-02 17:54:30  [{\"distance\": 5219, \"price\": 300, \"eta\": 1367,...   \n",
       "1  2018-11-16 10:53:10  [{\"distance\": 13864, \"price\": 600, \"eta\": 3221...   \n",
       "2  2018-10-06 10:33:58  [{\"distance\": 12294, \"price\": 400, \"eta\": 2472...   \n",
       "3  2018-11-23 14:54:11  [{\"distance\": 14853, \"price\": 1700, \"eta\": 291...   \n",
       "4  2018-10-30 11:48:41  [{\"distance\": 12882, \"price\": 600, \"eta\": 3211...   \n",
       "\n",
       "            click_time  click_mode  \n",
       "0  2018-11-02 17:54:32           9  \n",
       "1  2018-11-16 10:53:32           1  \n",
       "2  2018-10-06 10:34:23           9  \n",
       "3  2018-11-23 14:54:18           1  \n",
       "4  2018-10-30 11:49:04           7  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q_p_c.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "time = []\n",
    "for i in range(q_p_c.shape[0]):\n",
    "    req_time = datetime.datetime.strptime(q_p_c['req_time'][i], \"%Y-%m-%d %H:%M:%S\")\n",
    "    plan_time = datetime.datetime.strptime(q_p_c['plan_time'][i], \"%Y-%m-%d %H:%M:%S\")\n",
    "    click_time = datetime.datetime.strptime(q_p_c['click_time'][i], \"%Y-%m-%d %H:%M:%S\")\n",
    "    time.append(click_time-plan_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 结论：三个时间差距不大，只有个别时间差距比较大，可能是因为网络的问题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 四、profiles数据集"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1  数据集大小及类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "profiles = pd.read_csv('data_set_phase1//profiles.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pid</th>\n",
       "      <th>p0</th>\n",
       "      <th>p1</th>\n",
       "      <th>p2</th>\n",
       "      <th>p3</th>\n",
       "      <th>p4</th>\n",
       "      <th>p5</th>\n",
       "      <th>p6</th>\n",
       "      <th>p7</th>\n",
       "      <th>p8</th>\n",
       "      <th>...</th>\n",
       "      <th>p56</th>\n",
       "      <th>p57</th>\n",
       "      <th>p58</th>\n",
       "      <th>p59</th>\n",
       "      <th>p60</th>\n",
       "      <th>p61</th>\n",
       "      <th>p62</th>\n",
       "      <th>p63</th>\n",
       "      <th>p64</th>\n",
       "      <th>p65</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>196356</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>204083</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>170667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>115511</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>129719</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      pid   p0   p1   p2   p3   p4   p5   p6   p7   p8 ...   p56  p57  p58  \\\n",
       "0  196356  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0 ...   0.0  0.0  0.0   \n",
       "1  204083  1.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0 ...   0.0  0.0  0.0   \n",
       "2  170667  0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0 ...   0.0  0.0  0.0   \n",
       "3  115511  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0 ...   0.0  0.0  0.0   \n",
       "4  129719  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  1.0 ...   0.0  0.0  0.0   \n",
       "\n",
       "   p59  p60  p61  p62  p63  p64  p65  \n",
       "0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "1  1.0  0.0  0.0  0.0  1.0  0.0  0.0  \n",
       "2  0.0  0.0  0.0  1.0  0.0  0.0  0.0  \n",
       "3  0.0  0.0  0.0  0.0  1.0  0.0  0.0  \n",
       "4  0.0  1.0  0.0  0.0  0.0  0.0  0.0  \n",
       "\n",
       "[5 rows x 67 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profiles.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(63090, 67)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profiles.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pid      int64\n",
       "p0     float64\n",
       "p1     float64\n",
       "p2     float64\n",
       "p3     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profiles.dtypes[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pid    0\n",
       "p0     0\n",
       "p1     0\n",
       "p2     0\n",
       "p3     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profiles.isnull().sum()[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pid    63090\n",
       "p0         2\n",
       "p1         2\n",
       "p2         2\n",
       "p3         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profiles.nunique()[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 结论：\n",
    "#### 1、数据集有63090条数据\n",
    "#### 2、数据集中只有一个int64类型，其他都为float64类型\n",
    "#### 3、无缺失值\n",
    "#### 4、pid是唯一的"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 五、test_queries数据集\n",
    "### sid：会话id\n",
    "### pid：配置文件id\n",
    "### req_time：查询时间戳\n",
    "### o：起始点坐标\n",
    "### d：终点坐标"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1 数据集大小及类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_queries = pd.read_csv('data_set_phase1//test_queries.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sid</th>\n",
       "      <th>pid</th>\n",
       "      <th>req_time</th>\n",
       "      <th>o</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1126541</td>\n",
       "      <td>178395.0</td>\n",
       "      <td>2018-12-05 14:51:43</td>\n",
       "      <td>116.41,39.92</td>\n",
       "      <td>116.46,39.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>453685</td>\n",
       "      <td>179178.0</td>\n",
       "      <td>2018-12-01 12:50:33</td>\n",
       "      <td>116.39,39.90</td>\n",
       "      <td>116.41,39.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2066073</td>\n",
       "      <td>184748.0</td>\n",
       "      <td>2018-12-07 10:17:35</td>\n",
       "      <td>116.37,39.71</td>\n",
       "      <td>116.31,39.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>452746</td>\n",
       "      <td>162795.0</td>\n",
       "      <td>2018-12-01 07:52:44</td>\n",
       "      <td>116.33,39.97</td>\n",
       "      <td>116.31,39.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1431672</td>\n",
       "      <td>116384.0</td>\n",
       "      <td>2018-12-06 10:36:30</td>\n",
       "      <td>116.51,39.68</td>\n",
       "      <td>116.51,39.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sid       pid             req_time             o             d\n",
       "0  1126541  178395.0  2018-12-05 14:51:43  116.41,39.92  116.46,39.95\n",
       "1   453685  179178.0  2018-12-01 12:50:33  116.39,39.90  116.41,39.91\n",
       "2  2066073  184748.0  2018-12-07 10:17:35  116.37,39.71  116.31,39.89\n",
       "3   452746  162795.0  2018-12-01 07:52:44  116.33,39.97  116.31,39.98\n",
       "4  1431672  116384.0  2018-12-06 10:36:30  116.51,39.68  116.51,39.68"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_queries.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(94358, 5)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_queries.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sid           int64\n",
       "pid         float64\n",
       "req_time     object\n",
       "o            object\n",
       "d            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_queries.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sid             0\n",
       "pid         31447\n",
       "req_time        0\n",
       "o               0\n",
       "d               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_queries.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sid         94358\n",
       "pid         13299\n",
       "req_time    84156\n",
       "o            3257\n",
       "d            2884\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_queries.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 结论：\n",
    "#### 1、数据集中有94358条数据\n",
    "#### 2、数据集中有一个int64类型和一个float64类型以及三个object类型\n",
    "#### 3、pid缺失31447条数据，占33.32%\n",
    "#### 4、sid唯一\n",
    "#### 5、o和d，与训练集相似，应该是相同地点搜索的用户很多"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2  o和d分布探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "116.32,39.89    1133\n",
       "116.37,39.86    1059\n",
       "116.42,39.90    1044\n",
       "116.37,39.91     985\n",
       "116.41,39.91     980\n",
       "Name: o, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_queries['o'].value_counts()[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "116.32,39.89    3382\n",
       "116.37,39.86    2421\n",
       "116.42,39.90    2289\n",
       "116.37,39.91    1345\n",
       "116.39,39.90    1181\n",
       "Name: d, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_queries['d'].value_counts()[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from pandas import Series, DataFrame\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "\n",
    "o_x_list = []\n",
    "o_y_list = []\n",
    "\n",
    "d_x_list = []\n",
    "d_y_list = []\n",
    "\n",
    "# 将经纬度坐标提取出来,该部分可进行优化，参考Phase1_BaseLine\n",
    "for i in range(test_queries.shape[0]):\n",
    "    o_x_list.append(float(test_queries['o'][i].split(',')[0]))\n",
    "    o_y_list.append(float(test_queries['o'][i].split(',')[1]))\n",
    "    \n",
    "    d_x_list.append(float(test_queries['d'][i].split(',')[0]))\n",
    "    d_y_list.append(float(test_queries['d'][i].split(',')[1]))\n",
    "    \n",
    "o_data = pd.DataFrame.from_dict({'x': o_x_list, 'y': o_y_list})\n",
    "d_data = pd.DataFrame.from_dict({'x': d_x_list, 'y': d_y_list})\n",
    "\n",
    "# KMeans聚类\n",
    "model_o = KMeans(n_clusters=7, init='k-means++', n_init=5)\n",
    "model_d = KMeans(n_clusters=7, init='k-means++', n_init=5)\n",
    "y_pre_o = model_o.fit_predict(o_data)\n",
    "y_pre_d = model_d.fit_predict(d_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1db9a4c6b00>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画散点图图\n",
    "def plotScatter(data, y_pre, x_list, y_list):\n",
    "    matplotlib.rcParams['font.sans-serif'] = ['SimHei']\n",
    "    matplotlib.rcParams['axes.unicode_minus'] = False\n",
    "    cm = matplotlib.colors.ListedColormap(['c','b','r','g','m','y'])\n",
    "    plt.scatter(data['x'], data['y'], c=y_pre, cmap=cm, edgecolors='none')\n",
    "    x_min = np.min(x_list)\n",
    "    x_max = np.max(x_list)\n",
    "    y_min = np.min(y_list)\n",
    "    y_max = np.max(y_list)\n",
    "    plt.xticks(np.linspace(x_min,x_max,8,endpoint=True))\n",
    "    plt.yticks(np.linspace(y_min,y_max,9,endpoint=True))\n",
    "    plt.xlabel('纬度', fontsize=16)\n",
    "    plt.ylabel('经度', fontsize=16)\n",
    "plt.figure(figsize=(15, 10))\n",
    "ax1 = plt.subplot(2,2,1)\n",
    "plotScatter(o_data, y_pre_o, o_x_list, o_y_list)\n",
    "ax1.set_title('起点（o）经纬度分布', fontsize=18)\n",
    "ax2 = plt.subplot(2,2,2)\n",
    "plotScatter(d_data, y_pre_d, d_x_list, d_y_list)\n",
    "ax2.set_title('终点（d）经纬度分布', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 从上图可以看出，中心区较为密集，多为商场等大型场所，所以点很多；较远区域有很多散点，多为上班族下班回郊区所造成。而且起点（o）的分布与终点（d）的分布类似。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 六、test_plans数据集\n",
    "### sid：会话id\n",
    "### plan_time：计划推荐时间戳\n",
    "### plans：推荐模式，包括distance(距离m)、price(价格c)、eta(预计所需时间s)、transport_mode(出行模式)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.1 数据集的大小及类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_plans = pd.read_csv('data_set_phase1//test_plans.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sid</th>\n",
       "      <th>plan_time</th>\n",
       "      <th>plans</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1112456</td>\n",
       "      <td>2018-12-05 17:39:47</td>\n",
       "      <td>[{\"distance\": 465, \"price\": \"\", \"eta\": 418, \"t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1413458</td>\n",
       "      <td>2018-12-06 17:49:17</td>\n",
       "      <td>[{\"distance\": 28846, \"price\": 600, \"eta\": 4779...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1243160</td>\n",
       "      <td>2018-12-02 14:50:25</td>\n",
       "      <td>[{\"distance\": 24646, \"price\": 600, \"eta\": 3513...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2040494</td>\n",
       "      <td>2018-12-07 09:23:45</td>\n",
       "      <td>[{\"distance\": 32235, \"price\": 600, \"eta\": 4072...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1448779</td>\n",
       "      <td>2018-12-06 18:24:46</td>\n",
       "      <td>[{\"distance\": 13502, \"price\": 400, \"eta\": 3195...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sid            plan_time  \\\n",
       "0  1112456  2018-12-05 17:39:47   \n",
       "1  1413458  2018-12-06 17:49:17   \n",
       "2  1243160  2018-12-02 14:50:25   \n",
       "3  2040494  2018-12-07 09:23:45   \n",
       "4  1448779  2018-12-06 18:24:46   \n",
       "\n",
       "                                               plans  \n",
       "0  [{\"distance\": 465, \"price\": \"\", \"eta\": 418, \"t...  \n",
       "1  [{\"distance\": 28846, \"price\": 600, \"eta\": 4779...  \n",
       "2  [{\"distance\": 24646, \"price\": 600, \"eta\": 3513...  \n",
       "3  [{\"distance\": 32235, \"price\": 600, \"eta\": 4072...  \n",
       "4  [{\"distance\": 13502, \"price\": 400, \"eta\": 3195...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_plans.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(92571, 3)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_plans.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sid           int64\n",
       "plan_time    object\n",
       "plans        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_plans.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sid          0\n",
       "plan_time    0\n",
       "plans        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_plans.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sid          92571\n",
       "plan_time    82432\n",
       "plans        92163\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_plans.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 结论：\n",
    "#### 1、数据集一共有92571条数据\n",
    "#### 2、数据集中有一个int64类型，两个object类型\n",
    "#### 3、无缺失值\n",
    "#### 4、sid是唯一的，plan_time和plans有部分相同的"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.2 细节探索"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### test_queries和test_plans中都有sid和时间戳"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "q_p = pd.merge(test_queries, test_plans, on='sid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sid</th>\n",
       "      <th>pid</th>\n",
       "      <th>req_time</th>\n",
       "      <th>o</th>\n",
       "      <th>d</th>\n",
       "      <th>plan_time</th>\n",
       "      <th>plans</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1126541</td>\n",
       "      <td>178395.0</td>\n",
       "      <td>2018-12-05 14:51:43</td>\n",
       "      <td>116.41,39.92</td>\n",
       "      <td>116.46,39.95</td>\n",
       "      <td>2018-12-05 14:51:43</td>\n",
       "      <td>[{\"distance\": 13610, \"price\": 400, \"eta\": 2388...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>453685</td>\n",
       "      <td>179178.0</td>\n",
       "      <td>2018-12-01 12:50:33</td>\n",
       "      <td>116.39,39.90</td>\n",
       "      <td>116.41,39.91</td>\n",
       "      <td>2018-12-01 12:50:33</td>\n",
       "      <td>[{\"distance\": 2858, \"price\": 200, \"eta\": 1798,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2066073</td>\n",
       "      <td>184748.0</td>\n",
       "      <td>2018-12-07 10:17:35</td>\n",
       "      <td>116.37,39.71</td>\n",
       "      <td>116.31,39.89</td>\n",
       "      <td>2018-12-07 10:17:35</td>\n",
       "      <td>[{\"distance\": 33453, \"price\": 800, \"eta\": 5624...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>452746</td>\n",
       "      <td>162795.0</td>\n",
       "      <td>2018-12-01 07:52:44</td>\n",
       "      <td>116.33,39.97</td>\n",
       "      <td>116.31,39.98</td>\n",
       "      <td>2018-12-01 07:52:44</td>\n",
       "      <td>[{\"distance\": 4007, \"price\": 1500, \"eta\": 989,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1431672</td>\n",
       "      <td>116384.0</td>\n",
       "      <td>2018-12-06 10:36:30</td>\n",
       "      <td>116.51,39.68</td>\n",
       "      <td>116.51,39.68</td>\n",
       "      <td>2018-12-06 10:36:30</td>\n",
       "      <td>[{\"distance\": 692, \"price\": \"\", \"eta\": 591, \"t...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sid       pid             req_time             o             d  \\\n",
       "0  1126541  178395.0  2018-12-05 14:51:43  116.41,39.92  116.46,39.95   \n",
       "1   453685  179178.0  2018-12-01 12:50:33  116.39,39.90  116.41,39.91   \n",
       "2  2066073  184748.0  2018-12-07 10:17:35  116.37,39.71  116.31,39.89   \n",
       "3   452746  162795.0  2018-12-01 07:52:44  116.33,39.97  116.31,39.98   \n",
       "4  1431672  116384.0  2018-12-06 10:36:30  116.51,39.68  116.51,39.68   \n",
       "\n",
       "             plan_time                                              plans  \n",
       "0  2018-12-05 14:51:43  [{\"distance\": 13610, \"price\": 400, \"eta\": 2388...  \n",
       "1  2018-12-01 12:50:33  [{\"distance\": 2858, \"price\": 200, \"eta\": 1798,...  \n",
       "2  2018-12-07 10:17:35  [{\"distance\": 33453, \"price\": 800, \"eta\": 5624...  \n",
       "3  2018-12-01 07:52:44  [{\"distance\": 4007, \"price\": 1500, \"eta\": 989,...  \n",
       "4  2018-12-06 10:36:30  [{\"distance\": 692, \"price\": \"\", \"eta\": 591, \"t...  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q_p.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(92571, 7)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q_p.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 比较两个时间戳\n",
    "import datetime\n",
    "time = []\n",
    "for i in range(q_p.shape[0]):\n",
    "    req_time = datetime.datetime.strptime(q_p['req_time'][i], \"%Y-%m-%d %H:%M:%S\")\n",
    "    plan_time = datetime.datetime.strptime(q_p['plan_time'][i], \"%Y-%m-%d %H:%M:%S\")\n",
    "    time.append(req_time-plan_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 结论：和训练集类似，req_time和plan_time有99%是相同的"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 七、提交格式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.read_csv('data_set_phase1//submission_example.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1126541</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>453685</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2066073</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>452746</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1431672</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1362848</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   1126541  2\n",
       "0   453685  1\n",
       "1  2066073  7\n",
       "2   452746  2\n",
       "3  1431672  5\n",
       "4  1362848  1"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1126541    int64\n",
       "2          int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 官方给的提交格式没有header，需要自己加上去；提交格式有两个int64数据类型，注意提交格式。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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